class_docstr: Instantiates a network in order to get predictions from it.
  
  If a checkpoint exists in the job's directory, load it.  The names of the
  classes will be obtained from the dataset directory.
  
  Returns a list of objects detected, which is a dict of its coordinates,
  label and probability, ordered by probability.
code: def predict_image(self, image):
      fetched = self.session.run(
          self.fetches,
          feed_dict={self.image_placeholder: np.array(image)},
      )
  
      objects = fetched["objects"]
      labels = fetched["labels"].tolist()
      probs = fetched["probs"].tolist()
      scale_factor = fetched["scale_factor"]
  
      if self.class_labels is not None:
          labels = [self.class_labels[label] for label in labels]
  
      # Scale objects to original image dimensions
      if isinstance(scale_factor, tuple):
          # If scale factor is a tuple, it means we need to scale height and
          # width by a different amount. In that case scale factor is:
          # (scale_factor_height, scale_factor_width)
          objects /= [
              scale_factor[1],
              scale_factor[0],
              scale_factor[1],
              scale_factor[0],
          ]
      else:
          # If scale factor is a scalar, height and width get scaled by the
          # same amount
          objects /= scale_factor
  
      # Cast to int to consistently return the same type in Python 2 and 3
      objects = [
          [int(round(coord)) for coord in obj]
          for obj in objects.tolist()
      ]
  
      predictions = sorted(
          [
              {
                  "bbox": obj,
                  "label": label,
                  "prob": round(prob, 4),
              }
              for obj, label, prob in zip(objects, labels, probs)
          ],
          key=lambda x: x["prob"],
          reverse=True,
      )
  
      return predictions
  
file_path: luminoth/luminoth/utils/predicting.py
imports: import json
  import numpy
  import os
  import tensorflow
  from luminoth.models import get_model
  from luminoth.datasets import get_dataset
label_desc: Use image node_id 2 for calling the PredictorNetwork obj's underlying member method code with example usage: obj.predict_image(image) and returns: predictions